Real-Time Super-Resolution Ultrasound Imaging using GPU Acceleration
Bibliographic record
Abstract
The method SUper-Resolution ultrasound imaging using the Erythrocytes as targets (SURE) is fully non-invasive, and can reliably visualize vessels with sizes down to 50 μm (1/3 of the wavelength) from a few seconds of data acquisition. Ideally, the acquisition and display should be done in seconds, but this is a challenge since the processing of SURE images is computationally demanding. Graphics Processing Units (GPUs) are specialized for high-throughput parallel processing, and in this paper it was explored whether a NVIDIA GeForce RTX 3090 GPU can enable real-time processing of SURE images, meaning the processing can keep up with the imaging rate of 417 Hz, allowing for a live video feed, similar to conventional ultrasound imaging. In-vivo data was acquired from a Sprague-Dawley rat kidney with a 168 channel GE-L8-18iD 10 MHz linear array probe connected to a Verasonics Vantage 256 scanner at a 62.5 MHz sampling rate. The GPU was used to perform beamforming, motion correction, stationary echo cancellation and peak localization. The resulting processing rate was 475 Hz for the beamforming, and 497 Hz for the proceeding processing steps, resulting in a total rate of 239 Hz. Consequently, SURE images can now be acquired in 2 seconds, and shown approximately 1 second after this, making it possible to visualize super resolution images of the microvasculature at the bedside, for immediate diagnosis of the patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".